arXiv papers from November 2025

Shaunak Dhande, Chutian Ma, Giacinto Paolo Saggese, Paul Smith

Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, where missed failures incur costs roughly fifty times higher than false alarms. Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, wher

Hamza Tahboub, Weiyan Shi, Gang Hua, Huaizu Jiang

Understanding social interactions from visual cues is a fundamental challenge for a socially competent AI. While powerful pre-trained vision-language models (VLMs) have shown remarkable general capabilities, they surprisingly struggle to unify and learn multiple social perception tasks simultaneously, often exhibiting negative transfer. We identify that this

Hamza ElMokhtar Shili, Natasha Patnaik, Isabelle Ruble, Kathryn Jarjoura

We investigate algorithmic variants of the Frank-Wolfe (FW) optimization method for pruning convolutional neural networks. This is motivated by the "Lottery Ticket Hypothesis", which suggests the existence of smaller sub-networks within larger pre-trained networks that perform comparatively well (if not better). Whilst most literature in this area focuses on

Riyadh Mohammed Almushrafy

Micro-facial expressions are brief and involuntary facial movements that reflect genuine emotional states. While most prior work focuses on classifying discrete micro-expression categories, far fewer studies address the continuous evolution of intensity over time. Progress in this direction is limited by the lack of frame-level intensity labels, which makes

David A. Fehr, Hannes Kraus, Corey J. Cochrane, Michael E. Flatté

We present a quantitative theory for simulating optically detected magnetic resonance (ODMR) measurements of optically-active spin centers using steady-state Lindblad equations. We apply the theory to an experimental ODMR spectrum associated with the negatively-charged silicon vacancy V2 center in 6H-SiC, showing that spin Hamiltonian parameters, optical tra

Tina Torkaman, Yongquan Zhang

In this paper, we establish effective equidistribution of transverse intersection points between properly immersed totally geodesic submanifolds of complementary dimensions in a finite-volume hyperbolic manifold with respect to the hyperbolic volume measure, as the volume of the submanifolds tends to infinity.

Roman Geiko, Georgii Shuklin

We develop a framework for the classification of invertible translation-invariant stabilizer codes modulo condensation and stabilization with simple codes. We introduce generalizations of the Pauli groups of local unitaries for quantum systems of qudits on cubic lattices and analyze stabilizer Hamiltonians whose terms are chosen from these groups. We define

Muhammad Yousuf, Akshat Bagade, Chhittebbayi Penugonda, Maanas Baraya

Developers routinely work with source files whose variable names are generic or misleading, and with teams moving quickly, many functions are left undocumented. This slows comprehension, increases the risk of subtle bugs, and makes it harder for both humans and large language models (LLMs) to reason about code. We study variable name repair: given a real C++

Sanjiv K. Tiwari, Navdeep K. Panesar, Ronald L. Moore, Sabrina L. Savage

On April 17, 2024, the third successful Hi-C sounding rocket flight, Hi-C Flare, recorded coronal images in Fe XXI 129 A emission from 11 MK plasma during the post-maximum phase of an M1.6-class solar flare, achieving unprecedented spatial (~300 km) and temporal (1.3 s) resolutions. The flare started at 21:55 UT, peaked at 22:08 UT, and lasted ~40 minutes. H

Willem P Sijp

Australian house prices have risen strongly since the mid-1990s, but growth has been highly uneven across regions. Raw growth figures obscure whether these differences reflect persistent structural trends or cyclical fluctuations. We address this by estimating a three-factor model in levels for regional repeat-sales log price indexes over 1995-2024. The mode

Noah Fleming, Stefan Grosser, Siddhartha Jain, Jiawei Li

We initiate a systematic study of ${\sf TFZPP}$, the class of total ${\sf NP}$ search problems solvable by polynomial time randomized algorithms. ${\sf TFZPP}$ contains a variety of important search problems such as $\text{Bertrand-Chebyshev}$ (finding a prime between $N$ and $2N$), refuter problems for many circuit lower bounds, and $\text{Lossy-Code}$. The

Núria Fagella, Gustavo R. Ferreira, Leticia Pardo-Simón

We study the dynamical Teichm\"uller space ${\mathcal T}(U,f)$ associated to a wandering domain $U$ of an entire function $f$. We show that a discrete grand orbit relation in $U$ forces ${\mathcal T}(U,f)$ to be infinite dimensional, thereby answering a question of Fagella--Henriksen. We further describe the geometry of these spaces by developing normal form

Sizhe Fang, Deqiang Qiu

Multi-echo Gradient Echo (mGRE) sequences provide valuable quantitative parametric maps, such as Quantitative Susceptibility Mapping (QSM) and transverse relaxation rate (R2*), sensitive to tissue iron and myelin. However, structural morphometry typically relies on separate T1-weighted MPRAGE acquisitions, prolonging scan times. We propose a deep learning fr

Brandon Bonham, Amitava Bhattacharjee

The analogy between free-electron lasers (FELs) - laboratory devices which generate intense coherent light with tunable frequencies - and whistler wave-particle interactions in the magnetosphere has recently been extended to account for waves with spatially dependent amplitudes and a spectrum of frequencies. The whistler was found to be governed by one of th

Loris Mendolia, Chenxi Wen, Elisabetta Chicca, Giacomo Indiveri

Neuromorphic engineering makes use of mixed-signal analog and digital circuits to directly emulate the computational principles of biological brains. Such electronic systems offer a high degree of adaptability, robustness, and energy efficiency across a wide range of tasks, from edge computing to robotics. Within this context, we investigate a key feature of

Yunfei Zhang, Yizhuo He, Yuanxun Shao, Zhengtao Yao

Vision-Language Models (VLMs) have advanced multimodal understanding, yet still struggle when targets are embedded in cluttered backgrounds requiring figure-ground segregation. To address this, we introduce ChromouVQA, a large-scale, multi-task benchmark based on Ishihara-style chromatic camouflaged images. We extend classic dot plates with multiple fill geo

Luis Rodrigo Arnabal, Santiago Camara, Cecilia Dassatti

This paper studies how shocks to global banks' net worth transmit to Emerging Market Economies. Using the identification strategy of Ottonello and Song (2022), which isolates high-frequency surprises to banks' credit supply capacity, we show that positive shocks appreciate local currencies, lower external borrowing costs, increase capital flows to domestic b

Marti Masso Moreno, Carlos Arturo Perez-Alanis, P. K. Manoharan

We present a generalized elliptical cylindrical flux rope model for interplanetary coronal mass ejections (ICMEs) that allows for a non zero poloidal component in the internal magnetic field. We introduce a two step reconstruction algorithm that decouples the geometric configuration from the magnetic field fitting in order to improve numerical stability and

Ruchika Verma, Shrishtee Kandoi, Robina Afzal, Shengjia Chen

Foundation models have transformed computational pathology by providing generalizable representations from large-scale histology datasets. However, existing models are predominantly trained on surgical pathology data, which is enriched for non-nervous tissue and overrepresents neoplastic, inflammatory, metabolic, and other non-neurological diseases. Neuropat

Eric Gilbertson, Larry Signani, Kathryn Stanchak

The elevation of Mount Rainier was last surveyed in 2010 by the Land Surveyors Association of Washington. More recent LiDAR data and observational reports have indicated that the historical highest point on the mountain, Columbia Crest, has lost a significant amount of elevation and may no longer be the highest point. This report documents the results of mul

Federico Echenique, Anqi Li

We study misspecified Bayesian learning in principal-agent relationships, where an agent is assessed by an evaluator and rewarded by the market. The agent's outcome depends on their innate ability, costly effort -- whose effectiveness is governed by a productivity parameter -- and noise. The market infers the agent's ability from observed outcomes and reward

Azeez Idris, Abdurahman Ali Mohammed, Samuel Fanijo

Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation in

Haotian Liu, Haoyu Chen, Chenhui Pan, You Hu

Face forgery detection encompasses multiple critical tasks, including identifying forged images and videos and localizing manipulated regions and temporal segments. Current approaches typically employ task-specific models with independent architectures, leading to computational redundancy and ignoring potential correlations across related tasks. We introduce

Chen Henry Wu, Sachin Goyal, Aditi Raghunathan

Parallel sampling promises substantial gains in test-time scaling, but its effectiveness is sharply limited by diversity collapse, where models concentrate on a few modes and repeated samples produce the same mistakes. We propose the mode-conditioning (ModC) framework, which explicitly allocates test-time compute across reasoning modes using either specialis

Jishnu Goswami, Frithjof Karsch

We present lattice QCD results for ratios of net-baryon number cumulants along the pseudo-critical line and compare them with STAR measurements from the RHIC BES-II program. The ratio of first and second order cumulants, $R_{12}^B$, agrees well with corresponding net-proton number cumulants down to $\sqrt{s_{NN}}=11.5$ GeV or baryon chemical potentials $\mu_

W. Evans, T. Coussens, M. T. M. Woodley, A. M. Fabricant

The projected rapid growth of battery cell production over the next decade demands advanced diagnostic tools for quality control, ageing prediction, and recycling. Most existing techniques lack the spatial and temporal resolution required to capture internal electrochemical processes non-invasively. Here, we present magnetic imaging of current densities in b

Xiaoting Kuang, Boken Lin

Large Language Models (LLMs) excel at understanding context and qualitative nuances but struggle with the rigorous and transparent reasoning required in high-stakes quantitative domains such as financial trading. We propose a model-first hybrid architecture for the options "wheel" strategy that combines the strengths of LLMs with the robustness of a Bayesian

Miroslav Bulíček, Jens Frehse

In the elliptic theory for $p$-Laplacian-like problems, the H\"{o}lder continuity of solutions has been proven for problems arising as Euler--Lagrange equations of a convex potential with $p$-growth that additionally satisfies the splitting condition. In this article, we extend these results to the parabolic setting. We investigate nonlinear parabolic system

Christine Awofeso, Patrick Greaves, Oded Lachish, Felix Reidl

The $3$-admissibility of a graph is a promising measure to identify real-world networks that have an algorithmically favourable structure. We design an algorithm that decides whether the $3$-admissibility of an input graph~$G$ is at most~$p$ in time~\runtime and space~\memory, where $m$ is the number of edges in $G$ and $n$ the number of vertices. To the bes

Geigh Zollicoffer, Tanush Chopra, Mingkuan Yan, Xiaoxu Ma

AI systems deployed in the real world must contend with distractions and out-of-distribution (OOD) noise that can destabilize their policies and lead to unsafe behavior. While robust training can reduce sensitivity to some forms of noise, it is infeasible to anticipate all possible OOD conditions. To mitigate this issue, we develop an algorithm that leverage

Jaimie Krankel, Guowei Wayne Tu, Evgueni T. Filipov

Woven shell structures are beneficial for applications requiring lightweight, damage resilience, and design tunability, such as in wearable devices, soft robotics, and aerospace systems. A fundamental component of woven structures is the woven column. While the mechanical properties of a woven column can be determined using sophisticated finite element (FE)

Pablo Yepiz-Graciano, Gabriel Ramos-Ortiz, Roberto Ramírez-Alarcón

The phenomenon of Entangled Two-Photon Absorption (ETPA) presents a persistent controversy in the literature, evidenced by a wide disparity in the reported values for the $\sigma_E$ cross-sections. Much of this discrepancy is attributed to the difficulty in discriminating ETPA from various background processes that can mimic its signal, such as linear absorp

Yilan Zhang, Li Nanbo, Changchun Yang, Jürgen Schmidhuber

The integration of histology images and gene profiles has shown great promise for improving survival prediction in cancer. However, current approaches often struggle to model intra- and inter-modal interactions efficiently and effectively due to the high dimensionality and complexity of the inputs. A major challenge is capturing critical prognostic events th

Tao Zhang, Yevgeniy Vorobeychik

We study the design of a privatization mechanism and privacy accounting in the Pufferfish Privacy (PP) family. Specifically, motivated by the curse of dimensionality and lack of practical composition tools for iterative learning in the recent Renyi Pufferfish Privacy (RPP) framework, we propose Sliced Renyi Pufferfish Privacy (SRPP). SRPP preserves PP/RPP se

Yukinari Sumino

We propose a method that enables a direct experimental probe of the quarkonium wave function defined in potential nonrelativistic QCD (pNRQCD) using the three-body decay of the $B_c$ meson. We show that the momentum distribution of the spectator $c$-quark in the partonic decay is proportional to the absolute square of the momentum-space wave function of the

Dongyue Li, Zhenshuo Zhang, Minxuan Duan, Edgar Dobriban

Algorithmic reasoning -- the ability to perform step-by-step logical inference -- has become a core benchmark for evaluating reasoning in graph neural networks (GNNs) and large language models (LLMs). Ideally, one would like to design a single model capable of performing well on multiple algorithmic reasoning tasks simultaneously. However, this is challengin

Tarun Chitra

Autodeleveraging (ADL) is a last-resort loss socialization mechanism for perpetual futures venues. It is triggered when solvency-preserving liquidations fail. Despite the dominance of perpetual futures in the crypto derivatives market, with over \$60 trillion of volume in 2024, there has been no formal study of ADL. In this paper, we provide the first rigoro

Himanshi Lalwani, Hanan Salam

College students often face academic and life stressors affecting productivity, especially students with Attention Deficit Hyperactivity Disorder (ADHD) who experience executive functioning challenges. Conventional productivity tools typically demand sustained self-discipline and consistent use, which many students struggle with, leading to disruptive app-sw

Jonathan S. Kent, Eliana Stefani, Brian Plancher

Coordinating emergency responses in extreme environments, such as wildfires, requires resilient and high-bandwidth communication backbones. While autonomous aerial swarms can establish ad-hoc networks to provide this connectivity, the high risk of individual node attrition in these settings often leads to network fragmentation and mission-critical downtime.

P. D. Yepiz-Graciano, D. Salamanca-Roldán, H. Cruz-Ramírez, A. B. U'Ren

We report an optimized implementation of spectral-domain quantum optical coherence tomography (SD-QOCT) capable of acquiring axial scans (A-scans) of multilayer samples without in the absence of mechanical scanning, at an unprecedented speed. We demonstrate a proof-of-concept system that integrates a diffraction grating, a high-resolution intensified CCD cam

Yaxuan Ren, Krithika Ramesh, Yaxing Yao, Anjalie Field

In this work, we aim to clarify and reconcile metrics for evaluating privacy protection in text through a systematic survey. Although text anonymization is essential for enabling NLP research and model development in domains with sensitive data, evaluating whether anonymization methods sufficiently protect privacy remains an open challenge. In manually revie

Gabriel Olin, Lu Chen, Nayesha Gandotra, Maxim Likhachev

Intercepting fast moving objects, by its very nature, is challenging because of its tight time constraints. This problem becomes further complicated in the presence of sensor noise because noisy sensors provide, at best, incomplete information, which results in a distribution over target states to be intercepted. Since time is of the essence, to hit the targ

Sanjog Misra

Foundation models, and in particular large language models, can generate highly informative responses, prompting growing interest in using these ''synthetic'' outputs as data in empirical research and decision-making. This paper introduces the idea of a foundation prior, which shows that model-generated outputs are not as real observations, but draws from th

Nathan F. Lepora

What is the future of tactile robotics? To help define that future, this article provides a historical perspective on tactile sensing in robotics from the wealth of knowledge and expert opinion in nearly 150 reviews over almost half a century. This history is characterized by a succession of generations: 1965-79 (origins), 1980-94 (foundations and growth), 1

Himanshi Lalwani, Hanan Salam

College students often face academic challenges that hamper their productivity and well-being. Although self-help books and productivity apps are popular, they often fall short. Books provide generalized, non-interactive guidance, and apps are not inherently educational and can hinder the development of key organizational skills. Traditional productivity coa

Evelyn Zhang, Alex Richardson, Jonathan Sprinkle

The goal of this paper is to explore the accuracy of dashcam footage to predict the actual kinematic motion of a car-like vehicle. Our approach uses ground truth information from the vehicle's on-board data stream, through the controller area network, and a time-synchronized dashboard camera, mounted to a consumer-grade vehicle, for 18 hours of footage and d

Roy Velich, Arkadi Piven, David Bensaïd, Daniel Cremers

We introduce a novel framework that directly learns a spectral basis for shape and manifold analysis from unstructured data, eliminating the need for traditional operator selection, discretization, and eigensolvers. Grounded in optimal-approximation theory, we train a network to decompose an implicit approximation operator by minimizing the reconstruction er

Hui Zhou, Jiaying Guo, Marios Aristodemou, Zhaoyang Du

As mission-critical (MC) services such as Unmanned Aerial Vehicles (UAVs) based emergency communication and Internet of Vehicles (IoVs) enabled autonomous driving emerge, the traditional communication framework can not meet the growing demands for higher reliability and lower latency and the increasing transmission loads. Semantic Communication (SemCom), an

Fatemeh Khashami

We investigate quantum features and non-classical nature of two-spin-$1/2$ NMR systems at thermal equilibrium under external magnetic fields. More specifically, using suitable quantifiers, we analyze quantum coherence, mixedness, and entanglement in NMR systems and examine their features within the system. We derive closed-form analytical expressions for the

Daria Smirnova, Hamid Nasiri, Marta Adamska, Zhengxin Yu

As modern artificial intelligence (AI) systems become more advanced and capable, they can leverage a wide range of tools and models to perform complex tasks. The task of orchestrating these models is increasingly performed by Large Language Models (LLMs) that rely on qualitative descriptions of models for decision-making. However, the descriptions provided t

Mrdjan Jankovic, Shreshta Rajakumar Deshpande, Gopika Ajaykumar

In this paper we consider an interchange lane-swap scenario, a limited stretch of highway with two parallel lanes where most vehicles want to change lanes. We show that a particular decentralized Control Barrier Function based algorithm executes lane swaps efficiently, with minimal speed change, within the specified (short) road segment at high traffic densi

Zachary Terner, Alexander Petersen, Yuedong Wang

We introduce Smart Bayes, a new classification framework that bridges generative and discriminative modeling by integrating likelihood-ratio-based generative features into a logistic-regression-style discriminative classifier. From the generative perspective, Smart Bayes relaxes the fixed unit weights of Naive Bayes by allowing data-driven coefficients on de

Zifan Xu, Kristen Procko, Michael Munje, Kristin Patterson

In Fall 2023, we introduced a new AI Literacy class called The Essentials of AI for Life and Society (CS 109), a one-credit, seminar course consisting mainly of guest lectures, which was open to the entire university, including students, staff, and faculty. Building on its success and popularity, this paper describes our significant expansion of the course i

Fatih Merdan, Ozgur B. Akan

Molecular communication (MC) studies biological signals that are found in nature. Most MC literature focuses on particle properties, even though many natural phenomena exhibit wave-like behavior. One such signal is sound waves. Understanding how sound waves are used in nature can help us better utilize this signal in our interactions with our environment. To

Pranav Subbaraman, Shufan Li, Siyan Zhao, Aditya Grover

Masked Generative Models (MGM)s demonstrate strong capabilities in generating high-fidelity images. However, they need many sampling steps to create high-quality generations, resulting in slow inference speed. In this work, we propose Speed-RL, a novel paradigm for accelerating a pretrained MGMs to generate high-quality images in fewer steps. Unlike conventi

Taicheng Zheng, Dan Li, Jie Li

Multipurpose batch processes become increasingly popular in manufacturing industries since they adapt to low-volume, high-value products and shifting demands. These processes often operate in a dynamic environment, which faces disturbances such as processing delays and demand changes. To minimise long-term cost and system nervousness (i.e., disruptive change

Sofia Sideri, Georgia Troullinou, Elisjana Ymeralli, Vasilis Efthymiou

Property graphs have rapidly become the de facto standard for representing and managing complex, interconnected data, powering applications across domains from knowledge graphs to social networks. Despite the advantages, their schema-free nature poses major challenges for integration, exploration, visualization, and efficient querying. To bridge this gap, we

Ron Ziv, David Wei, Antonio Rubio-Abadal, Daniel Adler

Quantum many-body (QMB) systems are generally computationally hard: the computing resources necessary to simulate them exactly can often exceed the existing computation resources by orders of magnitude. For this reason, Richard Feynman proposed the concept of a quantum simulator: quantum systems engineered to obey a prescribed evolution equation and repeatin

Berkani Khaled, Zeraoulia Rafik

Malicious URLs remain a primary vector for phishing, malware, and cyberthreats. This study proposes a hybrid deep learning framework combining \texttt{HashingVectorizer} n-gram analysis, SMOTE balancing, Isolation Forest anomaly filtering, and a lightweight neural network classifier for real-time URL classification. The multi-stage pipeline processes URLs fr

Yannick A. D. Omar

There is increasing evidence that numerous membrane proteins can assemble into aggregates that modulate their function and affect many cellular processes such as signal transduction and endocytosis. Here, we present a theoretical description of the instantaneous translational diffusion coefficients of transmembrane protein aggregates on free and supported li

Dev Vyas

Mixture of Experts (MoE) architectures have demonstrated remarkable success in scaling neural networks, yet their application to continual learning remains fundamentally limited by a critical vulnerability: the learned gating network itself suffers from catastrophic forgetting. We introduce Mixture of Bidders (MoB), a novel framework that reconceptualizes ex

Jingxiang Huang, Samer Lahoud

With the rapid development of Internet of Things (IoT) technologies, the sub-GHz unlicensed spectrum is increasingly being shared by protocols such as Long Range (LoRa), Sigfox, and Long-Range Frequency-Hopping Spread Spectrum (LR-FHSS). These protocols must coexist within the same frequency bands, leading to mutual interference. This paper investigates the

Wouter van Doorn, Terence Tao

We answer several questions of Erd\H{o}s regarding sequences of natural numbers $A$ whose translates $n+A$ intersect with the squarefree numbers in various specified ways. For instance, we show that if every translate only contains finitely many squarefree numbers, then $A$ has zero density, although the decay rate of this density can be arbitrarily slow. On

Sattajit Barua, Rownak J. Mou, Koffi P. C. Yao

The role of additives such as FEC in extending the calendar life of silicon anodes beyond the cycling benefits is still not fully understood. Herein, the calendar life of high-loading Si (80 wt%) using baseline 1.2 M LiPF6 in EC-EMC electrolyte versus adding 10 wt% FEC is investigated over months. Over 8 days of aging, FEC leads to a 13-fold reduction in irr

Fatih E. Bilgen, A. Sila Okcu, O. Tansel Baydas, Ozgur B. Akan

Intelligent Reflecting Surfaces (IRS) are anticipated to serve as a key cornerstone of future wireless networks, providing an unmatched capability to deterministically shape electromagnetic wave propagation. Despite this potential, most existing research still considers the IRS merely as a standalone physical-layer component, controlled by transmitters. Howe

Praphull Kumar, Dean M. Townsley, Hunter Anz

White dwarfs (WDs) are the final fate of about 97\% of the stars in our galaxy, making them vital tracers of stellar history. A fraction of WDs exist in cataclysmic variable (CV) systems, accreting matter from a nearby companion star. A subset of CVs undergo episodic rapid mass transfer, termed dwarf novae (DNe) outbursts. Some accreting WDs exhibit near sin

Stephen Fitz

The Machine Consciousness Hypothesis states that consciousness is a substrate-free functional property of computational systems capable of second-order perception. I propose a research program to investigate this idea in silico by studying how collective self-models (coherent, self-referential representations) emerge from distributed learning systems embedde

Zhehao Yi, Rahul Bhadani

Barren plateaus, which means the training gradients become extremely small, pose a major challenge in optimizing parameterized quantum circuits, often making the learning process impractically slow or stall. This work shows why using neural networks to generate quantum circuit parameters helps overcome this difficulty. We introduce a geometric viewpoint that

Benhour Amirian, Ashley S. Dale, Sergei Kalinin, Jason Hattrick-Simpers

Accelerated material discovery increasingly relies on artificial intelligence and machine learning, collectively termed "AI/ML". A key challenge in using AI is ensuring that human scientists trust the models are valid and reliable. Accordingly, we define a trustworthy AI framework GIFTERS for materials science and discovery to evaluate whether reported machi

Lorenzo Guerrieri, Tymoteusz Chmiel, Xianglong Ni, Jerzy Weyman

Starting with a grade three perfect ideal $I \subset R$, we demonstrate how to produce the a self-dual resolution of length four using the resolution of the original ideal. This process is also reversible. The main case of interest is when the grade three perfect ideal has type two, so the output complex resolves $R/J$ for a grade four Gorenstein ideal $J$.

Jiawei Ren, Yan Zhuang, Xiaokang Ye, Lingjun Mao

While LLM/VLM-powered AI agents have advanced rapidly in math, coding, and computer use, their applications in complex physical and social environments remain challenging. Building agents that can survive and thrive in the real world (for example, by autonomously earning income or running a business) requires massive-scale interaction, reasoning, training, a

Neha Joshi, Pamir Gogoi, Aasim Mirza, Aayush Jansari

We present a culturally-grounded multimodal dataset of 1,060 traditional recipes crowdsourced from rural communities across remote regions of Eastern India, spanning 10 endangered languages. These recipes, rich in linguistic and cultural nuance, were collected using a mobile interface designed for contributors with low digital literacy. Endangered Language R

Abdella Mohameda, Christian Hendricksb, Xiangyu Hua

Growing interest in decarbonization and Arctic accessibility has renewed attention on Europe-Asia shipping corridors. The Northern Sea Route (NSR) is often portrayed as a 30-40% shortcut relative to Suez, with savings propagated to time, fuel, and CO2. The effect of enforcing sea-only feasibility on these baselines, and its downstream impact on time, fuel, a

Euclid Collaboration, A. Anselmi, R. Laureijs, G. D. Racca

The Euclid system performance is defined in terms of image quality metrics tuned to the weak gravitational lensing (WL) cosmological probe. WL induces stringent requirements on the shape and stability of the VIS instrument system point spread function (PSF). The PSF is affected by error contributions from the telescope, the focal plane and image motion, and

Faharudeen Alhassan, Hamed Karami, Amanda Bleichrodt, James M. Hyman

Accurate and reliable forecasting models are critical for guiding public health responses and policy decisions during pandemics such as COVID-19. Retrospective evaluation of model performance is essential for improving epidemic forecasting capabilities. In this study, we used COVID-19 wastewater data from CDC's National Wastewater Surveillance System to gene

K. Aafi, Z. El Fatouaki, A. Jabar, A. Tahiri

First-principles DFT calculations on the hydrides Ca2NiH6, Sr2NiH6, and Ba2NiH6 reveal key thermodynamic properties. These compounds exhibit increasing entropy and heat capacity with temperature, and are thermodynamically stable at elevated temperatures due to negative free energies. The kinetics of hydrogen storage is influenced by entropy changes during hy

Gaofeng Huang, Frank Kutzschebauch

In this survey paper we study parametric versions of writing a matrix in $SL_n (\mathbb{C})$ as a product of lower and upper unitriangular matrices in interchanging order as well as generalizations to other classical groups. We give an account of algebraic, continuous and holomorphic factorization results, from the standpoint of Several Complex Variables. Ou

Bongjung Sung

We study the geometry of the fixed-rank core covariance manifold arising from the Kronecker-core decomposition of covariance matrices. As shown in Hoff, McCormack, and Zhang (2023), every covariance matrix $\Sigma$ of $p_1\times p_2$ matrix-variate data uniquely decomposes into a separable component $K$ and a core component $C$. Such a decomposition also exi

Euzeli C. dos Santos, Josinaldo Lopes Araujo Rocha, Anielson dos Santos Souza, Isaac Soares de Freitas

In tropical semiarid regions, prickly pear cactus has emerged as a vital forage resource due to its high drought tolerance and minimal water requirements. However, even limited weed infestation can severely compromise cactus productivity, as the species are highly sensitive to competition for essential resources, which includes water, mineral nutrients, and

Benjamin Boe, Shadia Habbal, Miloslav Druckmüller, Pavel Štarha

We present narrowband observations of the Fe XIV (530.3 nm), Fe X (637.4 nm), and Fe XI (789.2 nm) coronal emission lines from the 2023 April 20 Total Solar Eclipse in Australia. We deployed pairs of telescopes for each emission line that were equipped with narrowband filters centered on, and several nanometers away from, the center wavelengths of the lines.

Evan Dramko, Yizhi Zhu, Aleksandar Krivokapic, Geoffroy Hautier

Accurate structural relaxation is critical for advanced materials design. Traditional approaches built on physics-derived first-principles calculations are computationally expensive, motivating the creation of machine-learning interatomic potentials (MLIPs), which strive to faithfully reproduce first-principles computed forces. We propose a fine-tuning metho

Hareeshwar Karthikeyan

Testing conversational AI systems at scale across diverse domains necessitates realistic and diverse user interactions capturing a wide array of behavioral patterns. We present a novel multi-agent framework for realistic, explainable human user simulation in interactive scenarios, using persona control and task state tracking to mirror human cognitive proces

Joel Cahn, Antonin Thomas, Philippe Pastor

This paper presents the development of a control law, which is intended to be implemented on an optical guided glider. This guiding law follows an innovative approach, the reinforcement learning. This control law is used to make navigation more flexible and autonomous in a dynamic environment. The final objective is to track a target detected with the camera

Carlos Martinez-Ranero, Lucas Polymeris

We construct a model of the Open Graph Axiom (OGA) in which there is a 2-entangled Suslin line $S$. Consequently, in this model, there is a 2-entangled uncountable linear order, but no such order is separable. This resolves a problem posed by Carroy, Levine, and Notaro \cite{carroy2025} and answers a question from McKenney on MathOverflow \cite{Mckenney2014}

Francisco J. Soulignac

We propose a simple and exact method for the Traveling Salesman Problem with Time Windows and Makespan objective (\TSPTW-M) that solves all instances of the classical benchmark with $50$ or more customers in less than ten seconds each. Applying this algorithm as an off-the-shelf method, we also solve all but one of these instances for the Duration objective.

Hikmatullo Ismatov

We give a self-contained and introductory account of some basic functional analytic tools needed to understand maximal monotone operators in Hilbert spaces. We review domains of (possibly unbounded) operators, closed sets and closed operators, and provide concrete examples of bounded and unbounded operators in both finite and infinite dimensions. We then exp

Seokhyun Chin, Junghwan Park, Woojin Cho

Precipitation nowcasting, key for early warning of disasters, currently relies on computationally expensive and restrictive methods that limit access to many countries. To overcome this challenge, we propose precipitation nowcasting using satellite imagery with physics constraints for improved accuracy and physical consistency. We use a novel physics-informe

Haoru Xue, Tairan He, Zi Wang, Qingwei Ben

Recent progress in GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning, where massive physics and visual randomization allow policies to generalize beyond curated environments. Building on these advances, we develop a teacher-student-bootstrap learning framework for vision-based humanoid loco-manipulation,

Fatemeh Khashami, Stefan Glöggler

We investigate quantum entanglement in two-spin-1/2 NMR systems at thermal equilibrium under external magnetic fields. We derive closed-form analytical expressions for the entanglement of the system and show how the entanglement depends on temperature and magnetic field strength, resulting in a threshold temperature beyond which entanglement vanishes. We dem

Anantha Padmanaban Krishna Kumar

Although scaling laws and many empirical results suggest that increasing the size of Vision Transformers often improves performance, model accuracy and training behavior are not always monotonically increasing with scale. Focusing on ViT-B/16 trained on ImageNet-1K, we study two simple parameter-reduction strategies applied to the MLP blocks, each removing 3

Youssof Abdullah, Violet Hart, Moumita Das

We investigate a conductance-based neuron model to explore how voltage-gated ion channel isoforms influence action-potential generation. The model combines a six-state Markov representation of NaV channels with a first-order KV3.1 model, allowing us to vary maximal sodium and potassium conductances and compare nine NaV isoforms. Using bifurcation theory and

Yihao Tan, Marianthi Markatou, Saptarshi Chakraborty

Monitoring the safety of medical products is a core concern of contemporary pharmacovigilance. To support drug safety assessment, Spontaneous Reporting Systems (SRS) collect reports of suspected adverse events of approved medical products offering a critical resource for identifying potential safety concerns that may not emerge during clinical trials. Modern

Shubham Aggarwal, Dipankar Maity, Tamer Başar

Accurate remote state estimation is a fundamental component of many autonomous and networked dynamical systems, where multiple decision-making agents interact and communicate over shared, bandwidth-constrained channels. These communication constraints introduce an additional layer of complexity, namely, the decision of when to communicate. This results in a

O. Jonathan Fajen, Joseph E. Kelly, Edward G. Hohenstein, Todd J. Martínez

Coupled cluster with singles, doubles and perturbative triples (CCSD(T)) often provides ground state correlation energies within "chemical accuracy," but suffers from high computational cost and steep scaling with system size. We present a GPU-accelerated implementation of CCSD(T) in the TeraChem software package. The new implementation achieves state-of-the

MohammadParsa Dini, Human Jafari

Machine Unlearning is essential for large generative models (VAEs, DDPMs) to comply with the right to be forgotten and prevent undesired content generation without costly retraining. Existing approaches, such as Static-lambda SISS for diffusion models, rely on a fixed mixing weight lambda, which is suboptimal because the required unlearning strength varies a

Luigi Foschini

1H 0323+342 is the nearest gamma-ray narrow-line Seyfert 1 galaxy (z=0.063). Its X-ray spectrum (0.3-10 keV) is characterised by significant spectral variability observed by many authors, with a backbone with photon index ~2 occasionally superimposed by a hard tail. This spectral variability has been interpreted as the interplay between the X-ray corona and

Muhtadin, Mochammad Hilmi Rusydiansyah, Mauridhi Hery Purnomo, I Ketut Eddy Purnama

Quadruped robots are increasingly used in various applications due to their high mobility and ability to operate in diverse terrains. However, most available quadruped robots are primarily focused on mobility without object manipulation capabilities. Equipping a quadruped robot with a robotic arm and gripper introduces a challenge in manual control, especial

Andy B. Zhang, Jason R. Reeves, David V. Martin, Veronica Pratt

Sunspots and solar flares are two different manifestations of magnetic activity on the surface of the Sun. On the Sun, flares typically occur close to spots. In this paper we test this the connection between spots and flares on other stars. We detect 218,386 stellar flares on 14,163 spotted stars using a new algorithm called \textsc{toffee}. Inhomogeneous sp

Hamoon Soleimani

Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between netw

Shamanth Sreekanth

While exploring dynamical systems, we often come across the principle of contraction mapping, or better known as the Banach fixed point theorem. It is an essential concept based on successive approximation, whose utility comes from two main guarantees: establishing existence and uniqueness of a solution, and establishing constructive proof. The intent of thi